Don’t laugh, but there may come a time when quantum computers are sorting out your grocery deliveries, and if Paul Clarke, CTO of the online food store Ocado is right, it could be sooner than you think.

In an interview with Ars Clarke revealed his interest in quantum computing to solve the huge mathematical problems that surround automating delivery services. In theory, quantum computing is well suited to probabilistic tasks and will outperform classical computing platforms in this area… just not yet.

Even so, a future move from a binary to a quantum method, while complicated, could ultimately optimise vehicle routing tasks, which feature numerous variables. It could also deliver a boost to Ocado’s forthcoming robot grid technology, optimising the 4D (space and time) conundrums that this much-touted but yet-to-be-unveiled system grapples with.

Enlarge/ The Ocado Service Platform continues to evolve: still in development is its own hive of robots that involve some intensive number crunching to ferry around goods to pickers and pre-empt demand.

In operation, a robot takes from the grid (or hive) a crate full of identical products, say, chocolate biscuits, to a picker who then takes the quantity needed for the customer basket and then the robot returns the crate back in the same position in the hive. However, it might choose to take it elsewhere if an upcoming order might need the same item.

So what happens next comes down to the algorithm’s savviness with time variables and placement, which is just the sort of 4D optimisation task quantum computing is good at. Indeed, having such a system might one day not just be the wishful thinking of a CTO but a necessity to realise a competitive advantage.

There’s something wrong with this picture though, and it’s not the bots or qubits: it’s the human element. All that sophistication for a crate to end up at a station for a human to grab the item and steadily complete a shopping list. Where’s the robot doing that job?

If only they could, as this is not a predetermined production line.

Even with AI, the problem is that robots and the neural networks that will imbue them with sufficient cleverness to perform a particular task have immense difficulty in learning new tricks while remembering old ones. So handling an inventory of over 48,000 items of all shapes, sizes, and consistencies would be a big ask.

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With simulation, combined with reinforcement learning, you could teach your robot arm how to pick biscuits from a crate—as they won’t always be in exactly the same place—and put them in the basket (or tote) with awareness of existing items and available space. If it needs to identify different products and intelligently operate with alternative policies for handling them, then it’s going to struggle. There’s the risk of catastrophic forgetting, a real term that describes when a previously learned thing is overwritten by the information gleaned from the latest task. Alas, you can train neural networks to do one thing well but transferring that knowledge from one task to another is a challenge.

Enlarge/ Raia Hadsell, senior research scientist at Google DeepMind appreciates that it can be difficult to get a lot of robotics data, in particular, for deep reinforcement learning. Simulation and progressive neural networks suggest a lot of the graft can be achieved before a real robot is needed.

Bob Dormon

At the Re•Work Deep Learning Summit in London last week, Raia Hadsell, senior research scientist at Google DeepMind emphasised this point. Referring to earlier research she gave examples of separate neural networks being used to classify images, play Atari games, and music generation by mimicking existing audio. But, she declared, “There is no neural network in the world (and no method right now) that can be trained to identify objects in images, play space invaders, and listen to music. This is a problem. If we’re really going to get to general artificial intelligence, we need something that can learn multiple tasks.”